Domino’s Pizza ranks among the world’s top restaurant brands. Domino’s AnyWare is the company’s name for their customers’ ability to order pizzas not just online and through mobile phone apps, but via smartwatches, TVs, car entertainment systems, and even social media platforms. Domino's wanted to integrate information from every channel — 85,000 structured and unstructured data sources in all — to get a single view of its customers and global operations.

Unfortunately, the IT architecture in place at Domino’s was preventing them from reaching those goals. Says Dan Djuric, Vice President, Global Infrastructure and Enterprise Information Management, “we had more than 11,000 business users, 35 data scientists, and marketing agencies — and all of them wanted to build their own database.”

According to Djuric, key reasons Domino’s selected Talend were greater freedom to scale with a more agile architecture, open-source flexibility, rapid implementation, cost-effective and understandable licensing, and predictability.

Using Talend in conjunction with another off-the-shelf MDM software solution, Domino’s identifies unique customers from millions of order transactions. They have an infrastructure that collects information from all the company’s point of sales systems and 26 supply chain centers, and through all its channels, including text messages, Twitter, Pebble, Android, and Amazon Echo. Data is fed into Domino’s Enterprise Management Framework, where it’s combined with enrichment data from a large number of third party sources, such as the United States Postal Service, as well as geocode information, demographic and competitive information.

Data integration is the process of combining data from different sources to create a complete, accurate, and up-to-date dataset for BI, data analysis, and other applications and business processes. It includes data replication, ingestion, and transformation to combine different types of data into a standard format to be stored in a target repository such as a data warehouse, data lake or data lakehouse.

There are lots of ways we can use data integration and quality scenarios, but below are seven everyday uses that most companies have. The list is not ordered by importance, market size, or strategic focus. Also, the use cases are not exclusive, and it's common for an organisation to use several at once. Let's look at seven ways to use Qlik and Talend.

Database-to-database synchronization is the mainstay use case for many of us at Qlik and Talend. The combined functionality offers you tremendous flexibility for whatever problem you're trying to solve. So whether you use basic data loading, real-time replication, or micro-batch updates, we've got you covered. In fact, database-to-database is most commonly used to solve the following issues:

Second use case focuses on data warehouse modernization, which refers to automating a cloud data warehouse’s design, deployment, and operation. Data warehouse automation offers faster time to market for new data warehouses, improves data quality, and reduces costs associated with manual administration.

Qlik’s secret sauce is the intelligent data pipelines that help organizations scale their data warehousing efforts more efficiently by automatically generating the necessary transformation SQL and pushing it down to the warehouse for execution.

No segment of the data integration market has seen so much change in recent years as the data lake. Consequently, there are many approaches to data lake implementation, and once again, Qlik's combined portfolio can support any architecture. Our data lake automation solutions help you move enterprise data, transform it, and enforce data governance policies to help you build a data lake for your data analytics, machine learning, and AI initiatives — regardless if your lake is based on Hadoop, cloud-object stores, or Databricks.

Integrating databases with streaming infrastructures like Apache Kafka or Amazon Kinesis can help organizations gain insights from their dynamic data and respond more quickly to changing business conditions. For example, a company might use a database to store customer data, such as their name, address, and purchase history. They could then use a streaming infrastructure like Kafka to process purchase data as it is generated in real time to highlight nefarious behavior such as fraudulent credit card transactions. Qlik's data integration and quality solutions can synchronize database transactions with streams and also source data from streams to route to any destination in virtually any format.

Accurate data is the lifeblood of any successful initiative that drives organizational excellence. Consequently, data quality is crucial to any business process, especially:

APIs are an intermediary layer that helps companies safely expose their application data and functionality to external third-party developers, business partners, and other company departments to encourage collaboration and drive innovation. Qlik and Talend's portfolio enable you to create and consume your own APIs for scenarios such as:

Last category in this list is operational data transformation. This converts raw data into formats that can be used by downstream processes such as electronic data exchange, data science, or analytics. Typically, operational data transformation occurs outside the data warehouse or lake, with the final files saved in an object store. An example: converting transactional records into HL7 files, transforming CSV files to Parquet, and converting aggregate data sources into EDI consumable formats. Qlik's data integration and quality solutions contain specialized functionality for many common transformations and will help you rapidly solve the data exchange problem for specific industry formats.

There are five different approaches to execute data integration: ETL, ELT, streaming, application integration (API) and data virtualization. To implement these processes, data engineers, architects and developers can either manually code an architecture using SQL or, more often, they set up and manage a data integration tool, which streamlines development and automates the system.

Each of these five approaches continue to evolve with the surrounding ecosystem of the modern data stack. Historically, data warehouses were the main target repositories, so data had to be transformed before loading. This is the classic ETL data pipeline (Extract > Transform > Load) and it’s still appropriate for small datasets that need complex transformations. However, with the rise of Integration Platform as a Service (iPaaS) solutions, larger datasets, data fabric and data mesh architectures, and the need to support real-time analytics and machine learning projects, integration is shifting from ETL to ELT, streaming and API.

Talend Cloud Services are online services that let you access Talend Software features and functions to design, manage, and monitor data integration capabilities. It has an architecture of multiple components, including Talend Studio, Talend Management Control, Talend Cloud Data Preparation and many more.

See the short introductory video about Talend Cloud capabilities (4,5 minutes):

For now, all you need is access to the Talend Cloud, where we explore platform capabilities with daily small hands-on practice tasks during Data Integration Week.
To start your Talend Cloud trial follow these steps:

After the initial setup and a short introductory video, Talend will ask you to choose one of the ways you want to interact with the product: as Analyst, ad Developer and as Self-Explorer.

Since our programme will give you tasks to complete, choose the third option and launch Talend Cloud! You will be able to come back to those materials if you need them via upper menu.

Great! Now you have access to Talend Cloud.

Watch this tutorial video and repeat the steps in this short video guide (6:14) with your dataset: